In order to locate obstacles, we used the data provided by the LADAR sensor mounted on the top of the robot. The data acquired gave us precise information on the distance of any object located in the range of the sensor. Knowing that the robot’s movement will be constrained within the walls of the course, we filtered every LADAR data that represented an x/y-coordinate within the course because this means that an obstacle was “hit” by the laser beam of the respective angular increment. To compensate for erroneous data, we split the course up in an 11 by 12 grid of tiles as indicated on the left. If the LADAR detects something within one of these tiles, we increased a tally counter within this tile. Once a certain threshold has been reached, the tile is declared to be an obstacle as visualized on the left side.
OBSTACLE AVOIDANCE
How does the robot know where the golf balls are? We used the video input from the camera and filtered the incoming data to register pixels in the HSV spectrum of the orange and blue golf balls. In order to obtain the specific spectra, we took pictures of the golf balls prior to the contest and adjusted the thresholds of the computer vision algorithm accordingly. As for the actual ball retrieval, the robot abandons the current path once a certain threshold of orange or blue pixels is exceeded. Then the centroid of the largest detected golf ball is calculated and the location of this centroid is fed back to the motor controller and a feedback control system is used to steer the robot towards the golf ball. At a certain distance, the golf ball disappears under the camera’s field of view and simple open loop control is utilized to make the robot go just far enough to collect the golf ball.
A simple gripper/trapper design is utilized to securely retrieve the golf balls. Once the robot is close enough, the front door of the gripper is opened and the inner flap is actuated in order to make the golf ball goes into the orange or blue compartment.
The one gate system simplified the code for control, and allowed us to more consistently collect the golf balls.
BALL COLLECTION
LABVIEW INTERFACE
Our LabVIEW begins by starting the communication with the robot and drawing the grid and boundaries of the track. While the robot is running, the locations of the robot and obstacles are plotted on the map. The previous positions of the robot can be viewed as well by clicking the “Display History” button so that its path can be viewed. When the robot collects the ball, a blue or orange dot is placed at where the robot picked it up. The LabVIEW identifies the color of the ball by using a variable passed from the robot. Depending on the color of collected ball, a value of either 1 or 2 is passed to the case structure. The coordinate of collected ball comes from the robot coordinate and is displayed in the table with its color.
THE TEAM
TONGXI YAN
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SHASHWAT GUPTA
JUNWU ZHANG​
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MICHAEL LIAO
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Path Planning
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Obstacle Avoidance
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Path Planning
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Ball Collection
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Path Planning
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Ball Collection
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Ball Collection
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LabView Interface
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Mechatronics
PATH PLANNING
In the field of path planning, there exist many different approaches and algorithms in order to find the “right path”. We decided to use the A* algorithm to create the fastest path with respect to the current goal.
We used Manhattan distance to calculate the cost of the path planning in order to avoid diagonal movements and running into obstacles. The position of the robot itself is calculated by merging the data from an optitrack camera system that was set up around the course with the dead reckoning data from the robot sensors using Kalman Filtering. The A* algorithm is called each time a target location has been reached or when a golf ball has been successfully retrieved. Furthermore, every time a new obstacle is located, A* is run when the obstacle is within the current path. This process repeats until the robot has reached the final destination.
